Advances in Technology Innovation 2022-09-22T09:49:42+08:00 The editorial office Open Journal Systems <p style="margin: 0cm 0cm 0pt;"><strong><em>Advances in Technology Innovation</em></strong> (AITI), ISSN 2518-2994 (Online), ISSN 2415-0436 (Print), is an international, multidiscipline, peer-reviewed scholarly journal. The official abbreviated title is <em><strong>Adv. technol. innov.</strong></em> It is dedicated to providing a platform for fast communication between the newest research works on the innovations of Technology &amp; Engineering. A paper will be online shortly once it is accepted and typeset. Currently, there is no publication charge, including article processing and submission charges. AITI is an open access journal which means that all contents are freely available without charge to the user or his/her institution.</p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">AITI is indexed by:</span></p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 26px;" src="" alt="" width="171" height="53">&nbsp; </span><img src="/public/site/images/ijeti/DOAJ4.png" alt=""> &nbsp;&nbsp; <img src="/public/site/images/ijeti/google5.png" alt=""> &nbsp; <img src="" alt="">&nbsp; <img src="/public/site/images/allen/ProQuest-41.png"> <img src="/public/site/images/ijeti/CAB_ABSTRACTS4.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/Resarch_Bible5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/WorldCat5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/allen/academia-12.png"> &nbsp;<img src="/public/site/images/ijeti/TOCs5.jpg" alt=""> &nbsp; <img src="/public/site/images/allen/Publons-22.5_1.png"> &nbsp;&nbsp;<img src="/public/site/images/allen/crossref3.png" width="92" height="42"></p> <p style="margin: 0cm 0cm 0pt;"><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">&nbsp;Under evaluation of SCI, EI(Compendex), INSPEC, etc.</span></p> <p style="margin: 0cm 0cm 0pt;">&nbsp;</p> Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3 2022-09-22T09:49:42+08:00 Saumya Salian Sudhir Sawarkar <p>The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F<sub>1</sub>-score of 88%.</p> 2022-09-22T09:43:47+08:00 Copyright (c) 2022 Saumya Salian, Sudhir Sawarkar Calculation of Temperature-Dependent Thermal Expansion Coefficient of Metal Crystals Based on Anharmonic Correlated Debye Model 2022-09-01T14:56:10+08:00 Tong Sy Tien Nguyen Thi Minh Thuy Vu Thi Kim Lien Nguyen Thi Ngoc Anh Do Ngọc Bich Le Quang Thanh <p>This study aims to calculate the anharmonic thermal expansion (TE) coefficient of metal crystals in the temperature dependence. The calculation model is derived from the anharmonic correlated Debye (ACD) model that is developed using the many-body perturbation approach and correlated Debye model based on the anharmonic effective potential. This potential has taken into account the influence on the absorbing and backscattering atoms of all their nearest neighbors in the crystal lattice. The numerical results for the crystalline zinc (Zn) and crystalline copper (Cu) are in agreement with those obtained by the other theoretical model and experiments at several temperatures. The analytical results show that the ACD model is useful and efficient in analyzing the TE of coefficient of metal crystals.</p> 2022-09-01T14:52:27+08:00 Copyright (c) 2022 Tong Sy Tien, Nguyen Thi Minh Thuy, Vu Thi Kim Lien, Nguyen Thi Ngoc Anh, Do Ngọc Bich, Le Quang Thanh Learning Representations for Face Recognition: A Review from Holistic to Deep Learning 2022-08-08T15:21:39+08:00 Fabian Barreto Jignesh Sarvaiya Suprava Patnaik <p>For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy.</p> 2022-08-05T00:00:00+08:00 Copyright (c) 2022 Fabian Barreto, Jignesh Sarvaiya, Suprava Patnaik An Experimental Study on the Mechanical Properties of Low-Aluminum and Rich-Iron-Calcium Fly Ash-Based Geopolymer Concrete 2022-08-08T15:21:39+08:00 Jack Widjajakusuma Ika Bali Gino Pranata Ng Kevin Aprilio Wibowo <p>Limited studies have been conducted on low-aluminum and rich-iron-calcium fly ash (LARICFA)-based geopolymer concrete with increased strength. This study aims to investigate the mechanical characteristics of LARICFA-based geopolymer concrete, including its compressive strength, split tensile strength, and ultimate moment. The steps of this study include material preparation and testing, concrete mix design and casting, specimen curing and testing, and the analysis of testing results. Furthermore, the specimen tests consist of the bending, compressive, and split tensile strength tests. The results show that the average compressive strength and the ultimate moment of the geopolymer concrete are 38.20 MPa and 22.90 kN·m, respectively, while the average ratio between the split tensile and compressive strengths is around 0.09. Therefore, the fly ash-based geopolymer concrete can be used in structural components.</p> 2022-07-27T00:00:00+08:00 Copyright (c) 2022 Jack Widjajakusuma, Ika Bali, Gino Pranata Ng, Kevin Aprilio Wibowo On the Estimation of the Mission Performance Index of Unmanned Surface Vehicles Based on the Mission Coverage Area 2022-07-20T09:36:47+08:00 Jae-Yong Lee Nam-Sun Son <p>For mission planning and replanning of multiple unmanned surface vehicles (USVs), it is important to estimate each USV’s mission performance in terms of sea surveillance (e.g., illegal ship control). In this study, a mission performance index (MPI) is proposed based on the mission coverage area for estimating the USVs’ mission performance of illegal ship control. The penalty value is considered in the MPI calculation procedure owing to the track-off of the USV. In addition, the USV simulation is conducted under illegal ship control, and the MPI is calculated based on changing the mission coverage area. The results show that the MPI increases with the path width of the mission coverage area.</p> 2022-07-20T09:36:42+08:00 Copyright (c) 2022 Jae-Yong Lee, Nam-Sun Son Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting 2022-08-08T15:21:39+08:00 Tran Thanh Ngoc Le Van Dai Lam Binh Minh <p>This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning (DL) models. The considered DL models are the convolutional neural network (CNN) and long short-term memory network (LSTMN). The procedure is made over (i) setting the configuration for CNN and LSTMN, (ii) establishing the hyperparameter values of CNN and LSTMN models based on epoch, batch, optimizer, dropout, filters, and kernel, (iii) using eight data standardization methods to standardize the input data, and (iv) using the GS algorithm to search the optimal hyperparameters based on the mean absolute error (MAE) and mean absolute percent error (MAPE) indexes. The effectiveness of the proposed method is verified on the power load data of the Australian state of Queensland and Vietnamese Ho Chi Minh city. The simulation results show that the proposed data standardization methods are appropriate, except for the zero-mean and min-max methods.</p> 2022-07-18T00:00:00+08:00 Copyright (c) 2022 Tran Thanh Ngoc, Le Van Dai, Lam Binh Minh